Showing 10 open source projects for "image morphing software"

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  • 1
    SageMaker Training Toolkit

    SageMaker Training Toolkit

    Train machine learning models within Docker containers

    Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and...
    Downloads: 0 This Week
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  • 2
    Laravel Media Library

    Laravel Media Library

    Associate files with Eloquent models

    This package can associate all sorts of files with Eloquent models. It provides a simple, fluent API to work with. The Pro version of the package offers Blade, Vue and React components to handle uploads to the media library and to administer the content of a medialibrary collection. The storage of the files is handled by Laravel's Filesystem, so you can use any filesystem you like. Additionally the package can create image manipulations on images and pdfs that have been added in the media...
    Downloads: 2 This Week
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  • 3
    torchvision

    torchvision

    Datasets, transforms and models specific to Computer Vision

    The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via...
    Downloads: 1 This Week
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  • 4
    AWS Deep Learning Containers

    AWS Deep Learning Containers

    A set of Docker images for training and serving models in TensorFlow

    AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference,...
    Downloads: 0 This Week
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  • 5
    TensorFlow.js models

    TensorFlow.js models

    Pretrained models for TensorFlow.js

    This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. New models should have a test NPM script. You...
    Downloads: 0 This Week
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  • 6
    Image classification models for Keras

    Image classification models for Keras

    Keras code and weights files for popular deep learning models

    All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'...
    Downloads: 1 This Week
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  • 7
    QPNet

    QPNet

    Synchronous Petri Nets' emulator

    QPNet (Quick Petri Net) is a fast Petri Nets' emulator, created by students of Moscow State Technical University of Radio Engineering, Electronics and Automation. It brings benefits for the educational process, while being under further development and extension. QPNet это быстрый эмулятор сетей Петри, созданный студентами МИРЭА. Он применяется в учебном процессе, а так же продолжает разрабатываться и эволюционировать.
    Downloads: 0 This Week
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  • 8

    set image plane

    press ctrl button with shelf button to toggle imageplane

    Sets Imageplane for your Autodesk Maya scene file, for the 3d Model you are working on. You must have this code in the shelf button : try: reload(setimgpln) except NameError: import setimgpln
    Downloads: 0 This Week
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  • 9
    Quantum Toolkit
    Quantum Mechanical Toolkit And 3D Viewer for C++. Allows Data Visualisation via Images, Surfaces and Volume plots using OpenGL, as well as rapid development Quantum Mechanical Simulations. Uses the Blitz++, VTK Visualisation and Qt Libraries.
    Downloads: 2 This Week
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    Freshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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  • 10
    The Shrimp suite provides interactive visualizations of graph-based data. Creole (Eclipse plug-in) visualizes Java source code. Jambalaya (Protege plug-in) visualizes ontologies (including OWL), & Stand-Alone Shrimp visualizes RSF/GXL/PRJ/PPRJ files.
    Downloads: 0 This Week
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